Overview

Dataset statistics

Number of variables15
Number of observations1117894
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory127.9 MiB
Average record size in memory120.0 B

Variable types

Categorical3
Numeric12

Alerts

zyklus_ is highly correlated with time_amin and 3 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 2 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 2 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 2 other fieldsHigh correlation
time_amin is highly correlated with zyklus_ and 5 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with zyklus_ and 4 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with zyklus_ and 3 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with zyklus_ and 5 other fieldsHigh correlation
time_pause_vorher is highly correlated with time_amin and 2 other fieldsHigh correlation
time_temp_hoch is highly correlated with temperature_amax and 2 other fieldsHigh correlation
time_temp_hoch_vorher is highly correlated with time_amin and 3 other fieldsHigh correlation
zyklus_ is highly correlated with time_amin and 3 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 1 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 1 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 1 other fieldsHigh correlation
time_amin is highly correlated with zyklus_ and 4 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with zyklus_ and 3 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with zyklus_ and 2 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with zyklus_ and 3 other fieldsHigh correlation
time_pause_vorher is highly correlated with time_amin and 1 other fieldsHigh correlation
zyklus_ is highly correlated with time_amin and 3 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 2 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 2 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 2 other fieldsHigh correlation
time_amin is highly correlated with zyklus_ and 4 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with zyklus_ and 3 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with zyklus_ and 3 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with zyklus_ and 4 other fieldsHigh correlation
time_pause_vorher is highly correlated with time_amin and 1 other fieldsHigh correlation
time_temp_hoch is highly correlated with temperature_amax and 2 other fieldsHigh correlation
comment_ is highly correlated with type_High correlation
type_ is highly correlated with comment_High correlation
batteryname_ is highly correlated with zyklus_ and 7 other fieldsHigh correlation
amperestunden is highly correlated with comment_ and 1 other fieldsHigh correlation
zyklus_ is highly correlated with batteryname_ and 6 other fieldsHigh correlation
comment_ is highly correlated with amperestunden and 2 other fieldsHigh correlation
type_ is highly correlated with amperestunden and 1 other fieldsHigh correlation
temperature_amax is highly correlated with batteryname_ and 2 other fieldsHigh correlation
temperature_amin is highly correlated with batteryname_ and 2 other fieldsHigh correlation
temperature_mean is highly correlated with batteryname_ and 2 other fieldsHigh correlation
time_amin is highly correlated with zyklus_ and 5 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with batteryname_ and 6 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with batteryname_ and 6 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with zyklus_ and 5 other fieldsHigh correlation
time_pause_vorher is highly correlated with batteryname_ and 6 other fieldsHigh correlation
time_temp_hoch is highly correlated with comment_High correlation
time_temp_hoch_vorher is highly correlated with batteryname_ and 6 other fieldsHigh correlation
time_temp_hoch is highly skewed (γ1 = 46.20583322) Skewed
amperestunden has 561175 (50.2%) zeros Zeros
time_temp_hoch has 475649 (42.5%) zeros Zeros

Reproduction

Analysis started2021-12-16 20:35:20.572309
Analysis finished2021-12-16 20:37:20.420741
Duration1 minute and 59.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

batteryname_
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
RW9
113578 
RW10
110818 
RW12
110013 
RW11
109389 
RW13
 
56049
Other values (23)
618047 

Length

Max length4
Median length4
Mean length3.799878164
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRW1
2nd rowRW1
3rd rowRW1
4th rowRW1
5th rowRW1

Common Values

ValueCountFrequency (%)
RW9113578
 
10.2%
RW10110818
 
9.9%
RW12110013
 
9.8%
RW11109389
 
9.8%
RW1356049
 
5.0%
RW1547720
 
4.3%
RW1445868
 
4.1%
RW2142700
 
3.8%
RW1642366
 
3.8%
RW2341694
 
3.7%
Other values (18)397699
35.6%

Length

2021-12-16T21:37:20.499625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rw9113578
 
10.2%
rw10110818
 
9.9%
rw12110013
 
9.8%
rw11109389
 
9.8%
rw1356049
 
5.0%
rw1547720
 
4.3%
rw1445868
 
4.1%
rw2142700
 
3.8%
rw1642366
 
3.8%
rw2341694
 
3.7%
Other values (18)397699
35.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amperestunden
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct133952
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0008962882368
Minimum-2.147068874
Maximum2.147192722
Zeros561175
Zeros (%)50.2%
Negative132910
Negative (%)11.9%
Memory size8.5 MiB
2021-12-16T21:37:20.609426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.147068874
5-th percentile-0.07607496247
Q10
median0
Q30.03333415301
95-th percentile0.1249883721
Maximum2.147192722
Range4.294261596
Interquartile range (IQR)0.03333415301

Descriptive statistics

Standard deviation0.1607122953
Coefficient of variation (CV)179.3087187
Kurtosis70.73545931
Mean0.0008962882368
Median Absolute Deviation (MAD)0
Skewness-5.806977835
Sum1001.955242
Variance0.02582844187
MonotonicityNot monotonic
2021-12-16T21:37:20.725642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0561175
50.2%
0.033334699456578
 
0.6%
0.033334426236554
 
0.6%
0.033334972685728
 
0.5%
0.033334153015289
 
0.5%
0.03333524594412
 
0.4%
0.058334426234166
 
0.4%
0.058334699454108
 
0.4%
0.024999453553951
 
0.4%
0.024999180333916
 
0.4%
Other values (133942)512017
45.8%
ValueCountFrequency (%)
-2.1470688741
< 0.1%
-2.1462390651
< 0.1%
-2.1434122191
< 0.1%
-2.1430242591
< 0.1%
-2.1427888181
< 0.1%
-2.1422263761
< 0.1%
-2.1397965621
< 0.1%
-2.138362831
< 0.1%
-2.1376205861
< 0.1%
-2.1374358471
< 0.1%
ValueCountFrequency (%)
2.1471927221
< 0.1%
2.1415824041
< 0.1%
2.1388317841
< 0.1%
2.1374373551
< 0.1%
2.1369088381
< 0.1%
2.1348230391
< 0.1%
2.1346482831
< 0.1%
2.1339161521
< 0.1%
2.1326148161
< 0.1%
2.1319957931
< 0.1%

zyklus_
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct113578
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32472.33152
Minimum0
Maximum113577
Zeros28
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2021-12-16T21:37:20.846649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1996
Q110087
median22971
Q344851
95-th percentile96975
Maximum113577
Range113577
Interquartile range (IQR)34764

Descriptive statistics

Standard deviation29157.94647
Coefficient of variation (CV)0.8979320274
Kurtosis0.2050848016
Mean32472.33152
Median Absolute Deviation (MAD)15201
Skewness1.106899109
Sum3.630062457 × 1010
Variance850185842.5
MonotonicityNot monotonic
2021-12-16T21:37:20.947859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
028
 
< 0.1%
473828
 
< 0.1%
474928
 
< 0.1%
474828
 
< 0.1%
474728
 
< 0.1%
474628
 
< 0.1%
474528
 
< 0.1%
474428
 
< 0.1%
474328
 
< 0.1%
474228
 
< 0.1%
Other values (113568)1117614
> 99.9%
ValueCountFrequency (%)
028
< 0.1%
128
< 0.1%
228
< 0.1%
328
< 0.1%
428
< 0.1%
528
< 0.1%
628
< 0.1%
728
< 0.1%
828
< 0.1%
928
< 0.1%
ValueCountFrequency (%)
1135771
< 0.1%
1135761
< 0.1%
1135751
< 0.1%
1135741
< 0.1%
1135731
< 0.1%
1135721
< 0.1%
1135711
< 0.1%
1135701
< 0.1%
1135691
< 0.1%
1135681
< 0.1%

comment_
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
rest (random walk)
520051 
discharge (random walk)
425240 
charge (random walk)
109833 
rest post random walk discharge
 
24699
charge (after random walk discharge)
 
21513
Other values (18)
 
16558

Length

Max length36
Median length20
Mean length20.78333813
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow current discharge at 0.04A
2nd rowlow current charge
3rd rowreference charge
4th rowreference discharge
5th rowreference charge

Common Values

ValueCountFrequency (%)
rest (random walk)520051
46.5%
discharge (random walk)425240
38.0%
charge (random walk)109833
 
9.8%
rest post random walk discharge24699
 
2.2%
charge (after random walk discharge)21513
 
1.9%
pulsed load (discharge)5068
 
0.5%
pulsed load (rest)5029
 
0.4%
reference charge950
 
0.1%
reference discharge950
 
0.1%
pulsed charge (charge)853
 
0.1%
Other values (13)3708
 
0.3%

Length

2021-12-16T21:37:21.068650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
random1101336
31.9%
walk1101336
31.9%
rest553151
16.0%
discharge479123
13.9%
charge135719
 
3.9%
post26900
 
0.8%
after21513
 
0.6%
pulsed12289
 
0.4%
load10583
 
0.3%
reference4182
 
0.1%
Other values (7)1287
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
R
561172 
D
423810 
C
132912 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowC
3rd rowC
4th rowD
5th rowC

Common Values

ValueCountFrequency (%)
R561172
50.2%
D423810
37.9%
C132912
 
11.9%

Length

2021-12-16T21:37:21.163217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-16T21:37:21.226155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
r561172
50.2%
d423810
37.9%
c132912
 
11.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

temperature_amax
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct41837
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-70.04370649
Minimum-4099.44775
Maximum59.78934
Zeros0
Zeros (%)0.0%
Negative28608
Negative (%)2.6%
Memory size8.5 MiB
2021-12-16T21:37:21.306384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile20.55078
Q126.2608
median31.74998
Q339.58841
95-th percentile47.6367
Maximum59.78934
Range4159.23709
Interquartile range (IQR)13.32761

Descriptive statistics

Standard deviation644.3293729
Coefficient of variation (CV)-9.198961695
Kurtosis34.93001804
Mean-70.04370649
Median Absolute Deviation (MAD)6.19929
Skewness-6.075094837
Sum-78301439.22
Variance415160.3408
MonotonicityNot monotonic
2021-12-16T21:37:21.424345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92713395
 
1.2%
-4094.098149175
 
0.8%
-4099.447753300
 
0.3%
-4059.53761978
 
0.2%
25.4008327
 
< 0.1%
25.92998312
 
< 0.1%
24.43581306
 
< 0.1%
36.97732303
 
< 0.1%
25.30741300
 
< 0.1%
24.99612300
 
< 0.1%
Other values (41827)1088198
97.3%
ValueCountFrequency (%)
-4099.447753300
 
0.3%
-4094.098149175
0.8%
-4093.92713395
1.2%
-4089.495612
 
< 0.1%
-4084.724371
 
< 0.1%
-4083.251461
 
< 0.1%
-4077.055181
 
< 0.1%
-4071.26051
 
< 0.1%
-4059.53761978
 
0.2%
-4055.581791
 
< 0.1%
ValueCountFrequency (%)
59.789341
< 0.1%
59.713311
< 0.1%
58.967712
< 0.1%
58.737731
< 0.1%
58.661682
< 0.1%
58.646472
< 0.1%
58.615782
< 0.1%
58.57162
< 0.1%
58.418762
< 0.1%
58.409672
< 0.1%

temperature_amin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct42013
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-75.54420326
Minimum-4099.44775
Maximum58.64647
Zeros0
Zeros (%)0.0%
Negative29984
Negative (%)2.7%
Memory size8.5 MiB
2021-12-16T21:37:21.534678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile19.77053
Q125.60313
median31.18026
Q338.86047
95-th percentile47.20129
Maximum58.64647
Range4158.09422
Interquartile range (IQR)13.25734

Descriptive statistics

Standard deviation659.0978893
Coefficient of variation (CV)-8.724665306
Kurtosis33.11310627
Mean-75.54420326
Median Absolute Deviation (MAD)6.28955
Skewness-5.923924832
Sum-84450411.56
Variance434410.0276
MonotonicityNot monotonic
2021-12-16T21:37:21.822721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92714537
 
1.3%
-4094.098149176
 
0.8%
-4099.447753432
 
0.3%
-4059.53762060
 
0.2%
23.81324324
 
< 0.1%
25.4008320
 
< 0.1%
24.99612312
 
< 0.1%
25.08951312
 
< 0.1%
25.28563311
 
< 0.1%
24.21791310
 
< 0.1%
Other values (42003)1086800
97.2%
ValueCountFrequency (%)
-4099.447753432
 
0.3%
-4094.098149176
0.8%
-4093.92714537
1.3%
-4089.495612
 
< 0.1%
-4088.132811
 
< 0.1%
-4086.087161
 
< 0.1%
-4084.724371
 
< 0.1%
-4083.251461
 
< 0.1%
-4077.055182
 
< 0.1%
-4073.646241
 
< 0.1%
ValueCountFrequency (%)
58.646471
 
< 0.1%
58.251021
 
< 0.1%
58.068522
< 0.1%
58.044592
< 0.1%
58.03812
< 0.1%
57.992471
 
< 0.1%
57.977262
< 0.1%
57.855583
< 0.1%
57.779542
< 0.1%
57.764332
< 0.1%

temperature_mean
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct571541
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.56557161
Minimum-4099.44775
Maximum58.64647
Zeros0
Zeros (%)0.0%
Negative29832
Negative (%)2.7%
Memory size8.5 MiB
2021-12-16T21:37:21.943106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile20.15407207
Q125.88329
median31.41023913
Q339.24945861
95-th percentile47.33323855
Maximum58.64647
Range4158.09422
Interquartile range (IQR)13.36616861

Descriptive statistics

Standard deviation651.9462108
Coefficient of variation (CV)-8.862110313
Kurtosis33.73386826
Mean-73.56557161
Median Absolute Deviation (MAD)6.223209131
Skewness-5.972256111
Sum-82238511.1
Variance425033.8618
MonotonicityNot monotonic
2021-12-16T21:37:22.052527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92712915
 
1.2%
-4094.098148819
 
0.8%
-4099.447753300
 
0.3%
-4059.53761968
 
0.2%
-4093.927480
 
< 0.1%
-4094.09814356
 
< 0.1%
23.81324169
 
< 0.1%
24.524169
 
< 0.1%
25.28563166
 
< 0.1%
25.4008162
 
< 0.1%
Other values (571531)1089390
97.5%
ValueCountFrequency (%)
-4099.447753300
 
0.3%
-4094.09814356
 
< 0.1%
-4094.098148819
0.8%
-4093.927480
 
< 0.1%
-4093.92712915
1.2%
-4093.8800911
 
< 0.1%
-4093.8300881
 
< 0.1%
-4093.8092221
 
< 0.1%
-4093.5108481
 
< 0.1%
-4093.437151
 
< 0.1%
ValueCountFrequency (%)
58.646471
< 0.1%
58.437281
< 0.1%
58.31898341
< 0.1%
58.258631
< 0.1%
58.167998691
< 0.1%
58.09918181
< 0.1%
58.068521
< 0.1%
58.050563931
< 0.1%
58.044592
< 0.1%
58.03811
< 0.1%

time_amin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1117397
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5779503.359
Minimum5.04
Maximum17607056.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2021-12-16T21:37:22.162473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.04
5-th percentile547129.167
Q12524378.915
median5335860.175
Q38458769.213
95-th percentile12524215.22
Maximum17607056.33
Range17607051.29
Interquartile range (IQR)5934390.298

Descriptive statistics

Standard deviation3901812.66
Coefficient of variation (CV)0.6751121018
Kurtosis-0.4682331604
Mean5779503.359
Median Absolute Deviation (MAD)2928068.46
Skewness0.5435026405
Sum6.460872128 × 1012
Variance1.522414203 × 1013
MonotonicityNot monotonic
2021-12-16T21:37:22.264854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.0411
 
< 0.1%
6373783.042
 
< 0.1%
1923777.72
 
< 0.1%
60.232
 
< 0.1%
1922613.162
 
< 0.1%
5056861.392
 
< 0.1%
7849722.382
 
< 0.1%
619398.222
 
< 0.1%
3525077.122
 
< 0.1%
11620776.832
 
< 0.1%
Other values (1117387)1117865
> 99.9%
ValueCountFrequency (%)
5.042
 
< 0.1%
5.11
 
< 0.1%
6.621
 
< 0.1%
60.0411
< 0.1%
60.051
 
< 0.1%
60.232
 
< 0.1%
60.251
 
< 0.1%
60.281
 
< 0.1%
1126.991
 
< 0.1%
1538.071
 
< 0.1%
ValueCountFrequency (%)
17607056.331
< 0.1%
17606756.31
< 0.1%
17603941.311
< 0.1%
17603889.541
< 0.1%
17603889.31
< 0.1%
17603829.331
< 0.1%
17603829.071
< 0.1%
17602256.611
< 0.1%
17602256.51
< 0.1%
17602255.631
< 0.1%

time_entladen_stark_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct144391
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180321.1266
Minimum0
Maximum594530.06
Zeros568
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2021-12-16T21:37:22.402333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18927.94
Q189388.45
median165653.73
Q3262154.47
95-th percentile362480.56
Maximum594530.06
Range594530.06
Interquartile range (IQR)172766.02

Descriptive statistics

Standard deviation114013.6962
Coefficient of variation (CV)0.6322814102
Kurtosis0.301324253
Mean180321.1266
Median Absolute Deviation (MAD)85273.94
Skewness0.6347070008
Sum2.015799055 × 1011
Variance1.299912292 × 1010
MonotonicityNot monotonic
2021-12-16T21:37:22.513441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0568
 
0.1%
300155
 
< 0.1%
151340.62150
 
< 0.1%
137434.53118
 
< 0.1%
600118
 
< 0.1%
31263.23114
 
< 0.1%
56564.46114
 
< 0.1%
98853.3112
 
< 0.1%
105957.72112
 
< 0.1%
242250.17110
 
< 0.1%
Other values (144381)1116223
99.9%
ValueCountFrequency (%)
0568
0.1%
0.014
 
< 0.1%
287.6832
 
< 0.1%
300155
 
< 0.1%
300.0141
 
< 0.1%
300.0211
 
< 0.1%
300.036
 
< 0.1%
347.682
 
< 0.1%
364.235
 
< 0.1%
407.682
 
< 0.1%
ValueCountFrequency (%)
594530.062
 
< 0.1%
594472.516
< 0.1%
594435.324
< 0.1%
594399.824
< 0.1%
594346.124
< 0.1%
594292.214
< 0.1%
594237.414
< 0.1%
594230.132
 
< 0.1%
594170.134
< 0.1%
594168.884
< 0.1%

time_entladen_leicht_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct279113
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1274444.71
Minimum0
Maximum4369229.81
Zeros37
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2021-12-16T21:37:22.639933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72243.49
Q1318299.15
median830223.495
Q31997053.365
95-th percentile3883537.143
Maximum4369229.81
Range4369229.81
Interquartile range (IQR)1678754.215

Descriptive statistics

Standard deviation1206459.564
Coefficient of variation (CV)0.9466550837
Kurtosis-0.1877695372
Mean1274444.71
Median Absolute Deviation (MAD)597608.915
Skewness1.023328297
Sum1.424694095 × 1012
Variance1.455544679 × 1012
MonotonicityNot monotonic
2021-12-16T21:37:22.740041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
378415.2368
 
< 0.1%
561542.0964
 
< 0.1%
392498.2462
 
< 0.1%
155801.562
 
< 0.1%
272910.4260
 
< 0.1%
510747.458
 
< 0.1%
31050.8358
 
< 0.1%
472302.9456
 
< 0.1%
1952315.0756
 
< 0.1%
4075528.3854
 
< 0.1%
Other values (279103)1117296
99.9%
ValueCountFrequency (%)
037
< 0.1%
7538.122
 
< 0.1%
7539.592
 
< 0.1%
7542.952
 
< 0.1%
7552.62
 
< 0.1%
7610.037
 
< 0.1%
7616.337
 
< 0.1%
7629.057
 
< 0.1%
7634.987
 
< 0.1%
7661.047
 
< 0.1%
ValueCountFrequency (%)
4369229.816
 
< 0.1%
4369111.1320
< 0.1%
4369109.662
 
< 0.1%
4368969.858
 
< 0.1%
4368669.854
 
< 0.1%
4368612.454
 
< 0.1%
4368611.642
 
< 0.1%
4368565.714
< 0.1%
4368565.212
 
< 0.1%
4368556.954
 
< 0.1%

time_laden_stark_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89181
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2818229.002
Minimum0
Maximum10915483.91
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2021-12-16T21:37:22.895731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile205758.65
Q11198590.79
median2562775.87
Q34068391.62
95-th percentile6747550.87
Maximum10915483.91
Range10915483.91
Interquartile range (IQR)2869800.83

Descriptive statistics

Standard deviation2025813.435
Coefficient of variation (CV)0.7188249902
Kurtosis1.050251071
Mean2818229.002
Median Absolute Deviation (MAD)1430975.66
Skewness0.9537177129
Sum3.150481292 × 1012
Variance4.103920072 × 1012
MonotonicityNot monotonic
2021-12-16T21:37:23.000777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30926.873336
 
0.3%
45485.583332
 
0.3%
47752.613072
 
0.3%
65091.732048
 
0.2%
64965.141838
 
0.2%
50677.121752
 
0.2%
107133.97148
 
< 0.1%
50763.51142
 
< 0.1%
660927.42140
 
< 0.1%
142718.24140
 
< 0.1%
Other values (89171)1101946
98.6%
ValueCountFrequency (%)
010
< 0.1%
1627.082
 
< 0.1%
6289.182
 
< 0.1%
9394.744
 
< 0.1%
9440.274
 
< 0.1%
9440.84
 
< 0.1%
9453.074
 
< 0.1%
9489.584
 
< 0.1%
9606.34
 
< 0.1%
9657.974
 
< 0.1%
ValueCountFrequency (%)
10915483.9112
< 0.1%
10906299.3616
< 0.1%
10899303.9610
< 0.1%
10892484.1710
< 0.1%
10869491.3218
< 0.1%
10850409.364
 
< 0.1%
10846895.287
 
< 0.1%
10839779.6114
< 0.1%
10839556.9712
< 0.1%
10833802.618
< 0.1%

time_pause_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct560650
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean889490.5416
Minimum0
Maximum5770552.51
Zeros71
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2021-12-16T21:37:23.126934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46880.21
Q1217789.74
median649647.84
Q31163133.353
95-th percentile2651651.051
Maximum5770552.51
Range5770552.51
Interquartile range (IQR)945343.6125

Descriptive statistics

Standard deviation898308.0015
Coefficient of variation (CV)1.009912933
Kurtosis4.315897176
Mean889490.5416
Median Absolute Deviation (MAD)462551.215
Skewness1.876990027
Sum9.943561395 × 1011
Variance8.069572656 × 1011
MonotonicityNot monotonic
2021-12-16T21:37:23.245548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071
 
< 0.1%
600054
 
< 0.1%
30034
 
< 0.1%
1860032
 
< 0.1%
2580032
 
< 0.1%
2460032
 
< 0.1%
2220032
 
< 0.1%
2100032
 
< 0.1%
1980032
 
< 0.1%
1620032
 
< 0.1%
Other values (560640)1117511
> 99.9%
ValueCountFrequency (%)
071
< 0.1%
30034
< 0.1%
120022
 
< 0.1%
240022
 
< 0.1%
360022
 
< 0.1%
480022
 
< 0.1%
570032
< 0.1%
600054
< 0.1%
720022
 
< 0.1%
840022
 
< 0.1%
ValueCountFrequency (%)
5770552.512
< 0.1%
5770552.352
< 0.1%
5770552.172
< 0.1%
5766952.172
< 0.1%
5766951.862
< 0.1%
5766951.682
< 0.1%
5766951.462
< 0.1%
5763351.462
< 0.1%
5763351.232
< 0.1%
5759751.232
< 0.1%

time_temp_hoch
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct27116
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.2959911
Minimum0
Maximum169171.2
Zeros475649
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2021-12-16T21:37:23.382016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q338.54
95-th percentile300
Maximum169171.2
Range169171.2
Interquartile range (IQR)38.54

Descriptive statistics

Standard deviation977.7642105
Coefficient of variation (CV)7.33528595
Kurtosis6415.326839
Mean133.2959911
Median Absolute Deviation (MAD)0.2
Skewness46.20583322
Sum149010788.6
Variance956022.8513
MonotonicityNot monotonic
2021-12-16T21:37:23.496658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0475649
42.5%
60167120
 
14.9%
30056626
 
5.1%
0.8632071
 
2.9%
0.0115343
 
1.4%
0.1812426
 
1.1%
0.1712138
 
1.1%
0.1610721
 
1.0%
360010698
 
1.0%
0.210685
 
1.0%
Other values (27106)314417
28.1%
ValueCountFrequency (%)
0475649
42.5%
0.0115343
 
1.4%
0.024937
 
0.4%
0.03796
 
0.1%
0.042424
 
0.2%
0.05546
 
< 0.1%
0.06390
 
< 0.1%
0.07301
 
< 0.1%
0.08205
 
< 0.1%
0.09160
 
< 0.1%
ValueCountFrequency (%)
169171.21
< 0.1%
166952.011
< 0.1%
166526.961
< 0.1%
166483.781
< 0.1%
162000.771
< 0.1%
156799.191
< 0.1%
154401.041
< 0.1%
154087.971
< 0.1%
136924.891
< 0.1%
134432.071
< 0.1%

time_temp_hoch_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct642226
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2501567.533
Minimum0
Maximum11416948.64
Zeros684
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2021-12-16T21:37:23.606653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile188048.3215
Q1663568.01
median1485936.5
Q33736390.243
95-th percentile7798453.685
Maximum11416948.64
Range11416948.64
Interquartile range (IQR)3072822.233

Descriptive statistics

Standard deviation2419130.569
Coefficient of variation (CV)0.9670458772
Kurtosis0.2881428619
Mean2501567.533
Median Absolute Deviation (MAD)1080925.04
Skewness1.163548805
Sum2.796487336 × 1012
Variance5.852192711 × 1012
MonotonicityNot monotonic
2021-12-16T21:37:23.732389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405611.6613208
 
1.2%
405011.4612024
 
1.1%
1341971.389277
 
0.8%
1578427.29179
 
0.8%
1497579.637161
 
0.6%
1469602.176428
 
0.6%
3739006.825021
 
0.4%
801984.514898
 
0.4%
1682809.074749
 
0.4%
801217.414205
 
0.4%
Other values (642216)1041744
93.2%
ValueCountFrequency (%)
0684
0.1%
30016
 
< 0.1%
300.171
 
< 0.1%
300.181
 
< 0.1%
300.191
 
< 0.1%
300.221
 
< 0.1%
300.241
 
< 0.1%
300.581
 
< 0.1%
300.781
 
< 0.1%
301.221
 
< 0.1%
ValueCountFrequency (%)
11416948.641
< 0.1%
11409748.641
< 0.1%
11406004.181
< 0.1%
11405704.181
< 0.1%
11394828.51
< 0.1%
11394648.671
< 0.1%
11394647.791
< 0.1%
11389247.791
< 0.1%
11389247.011
< 0.1%
11385647.011
< 0.1%

Interactions

2021-12-16T21:37:11.312515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:35:59.375594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:04.896536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:11.690617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:19.605873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:25.327452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:31.545973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:38.120260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:46.342960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:52.913152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:59.376444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:05.512319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:11.813237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:35:59.838853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:05.423702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:12.289891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:20.043582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:25.800655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:32.037982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:38.765932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:46.899976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:53.434480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:00.061758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:05.987409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:12.294667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:00.277366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:05.892986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:13.281721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:20.495706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:26.369184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:32.622957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:39.483676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:47.443111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:53.960344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:00.572166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:06.494188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:12.801698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:00.758764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:06.393532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:13.822746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:20.935703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:26.917115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:33.164659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:40.188916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:48.001762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:54.552927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:01.076387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:06.996508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:13.332824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:01.214091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:06.846053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:14.390029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:21.391629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:27.457948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:33.828160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:40.928537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:48.648292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:55.087542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:01.535123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:07.460727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:13.859785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:01.662428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:07.305052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:15.098260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:21.840319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:27.988098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:34.306182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:41.747100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:49.262472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:55.620756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:02.057060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:07.962048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:14.411953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:02.126817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:07.802500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:15.796117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:22.289620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:28.489511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:34.832519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:42.476025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:49.821053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:56.149714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:02.570978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:08.424124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:15.040530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:02.569242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:08.457436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:16.480045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:22.726536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:28.972872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:35.337842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:43.163983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:50.336128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:56.657703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:03.095745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:08.903175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:15.510420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:03.017302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:09.200997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:17.099509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:23.208354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:29.489351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:35.850244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:43.815356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:50.818973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:57.281774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:03.582185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:09.402552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:15.948524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:03.449152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:09.775572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:17.743902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:23.676182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:29.998710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:36.358262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:44.492612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:51.363576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:57.812527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:04.054703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:09.864593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:16.387843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:03.900600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:10.438992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:18.481842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:24.238273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:30.511967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:36.865872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:45.185097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:51.885680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:58.284784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:04.571621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:10.326145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:16.813845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:04.375520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:11.035673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:19.102171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:24.841519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:31.005504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:37.387545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:45.755520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:52.381914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:36:58.859946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:05.046387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-16T21:37:10.823184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-16T21:37:23.842840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-16T21:37:24.035566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-16T21:37:24.223418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-16T21:37:24.402571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-16T21:37:24.523526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-16T21:37:17.163352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-16T21:37:18.061943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

batteryname_amperestundenzyklus_comment_type_temperature_amaxtemperature_amintemperature_meantime_amintime_entladen_stark_vorhertime_entladen_leicht_vorhertime_laden_stark_vorhertime_pause_vorhertime_temp_hochtime_temp_hoch_vorher
0RW12.0949970low current discharge at 0.04AD18.7997217.0831317.9392681538.070.0188976.680.000.00.00.0
1RW1-2.1205491low current chargeC18.6422317.0673817.867832190514.750.0188976.68191271.640.00.00.0
2RW1-0.0210992reference chargeC18.3745118.0910318.232178381786.290.0188976.68194718.280.00.00.0
3RW12.0003643reference dischargeD23.5674218.3902521.798768385232.920.0196178.50194718.280.00.00.0
4RW1-2.0051604reference chargeC25.2826417.6973220.055134392434.740.0196178.50207816.730.00.00.0
5RW12.0002505reference dischargeD24.0663918.1540321.449898405533.190.0203379.98207816.730.00.00.0
6RW10.0000006pulsed load (rest)R18.3587617.8233118.001093425723.630.0203379.98207816.731200.00.00.0
7RW10.1666497pulsed load (discharge)D19.8391218.2327719.234778426923.660.0203979.98207816.731200.00.00.0
8RW10.0000008pulsed load (rest)R19.8391218.7682219.351673427523.630.0203979.98207816.732400.00.00.0
9RW10.1666529pulsed load (discharge)D20.9789919.2406720.080216428723.660.0204579.98207816.732400.00.00.0

Last rows

batteryname_amperestundenzyklus_comment_type_temperature_amaxtemperature_amintemperature_meantime_amintime_entladen_stark_vorhertime_entladen_leicht_vorhertime_laden_stark_vorhertime_pause_vorhertime_temp_hochtime_temp_hoch_vorher
1117884RW90.000000113568rest (random walk)R38.8014638.7860038.79373012641068.65319549.854139735.625113269.551093976.631.149.360014e+06
1117885RW90.062502113569discharge (random walk)D38.8787538.3222438.63716512641069.82319549.854140035.625113269.551093976.63300.009.360314e+06
1117886RW9-1.077257113570reference chargeC38.3222431.3657633.21899512641369.82319549.854140035.625144349.951093976.6331080.409.391394e+06
1117887RW90.000000113571rest post reference chargeR32.4015132.3705932.38156212672450.19319549.854140035.625144349.951094276.63300.009.391694e+06
1117888RW90.748778113572reference dischargeD38.2913232.4015135.71939312672750.22319549.854142731.525144349.951094276.632695.909.394390e+06
1117889RW90.000000113573rest post reference dischargeR38.2913230.7319532.87481312675446.09319549.854142731.525144349.951101476.637200.009.401590e+06
1117890RW9-0.774939113574reference chargeC34.1329030.7628732.24254812682646.12319549.854142731.525164882.661101476.6320532.719.422123e+06
1117891RW90.000000113575rest post reference chargeR32.4788032.4015132.44638512703178.80319549.854142731.525164882.661101776.63300.009.422423e+06
1117892RW90.750203113576reference dischargeD38.8632932.4633435.95851412703478.83319549.854145432.365164882.661101776.632700.849.425124e+06
1117893RW90.000000113577rest post reference dischargeR38.8632932.1541733.76602112706179.64319549.854145432.365164882.661108976.637200.009.432324e+06